from typing import List, Tuple import torch import torch.nn.functional as F from torch import nn from tqdm.auto import tqdm from TTS.tts.layers.tacotron.common_layers import Linear from TTS.tts.layers.tacotron.tacotron2 import ConvBNBlock class Encoder(nn.Module): r"""Neural HMM Encoder Same as Tacotron 2 encoder but increases the input length by states per phone Args: num_chars (int): Number of characters in the input. state_per_phone (int): Number of states per phone. in_out_channels (int): number of input and output channels. n_convolutions (int): number of convolutional layers. """ def __init__(self, num_chars, state_per_phone, in_out_channels=512, n_convolutions=3): super().__init__() self.state_per_phone = state_per_phone self.in_out_channels = in_out_channels self.emb = nn.Embedding(num_chars, in_out_channels) self.convolutions = nn.ModuleList() for _ in range(n_convolutions): self.convolutions.append(ConvBNBlock(in_out_channels, in_out_channels, 5, "relu")) self.lstm = nn.LSTM( in_out_channels, int(in_out_channels / 2) * state_per_phone, num_layers=1, batch_first=True, bias=True, bidirectional=True, ) self.rnn_state = None def forward(self, x: torch.FloatTensor, x_len: torch.LongTensor) -> Tuple[torch.FloatTensor, torch.LongTensor]: """Forward pass to the encoder. Args: x (torch.FloatTensor): input text indices. - shape: :math:`(b, T_{in})` x_len (torch.LongTensor): input text lengths. - shape: :math:`(b,)` Returns: Tuple[torch.FloatTensor, torch.LongTensor]: encoder outputs and output lengths. -shape: :math:`((b, T_{in} * states_per_phone, in_out_channels), (b,))` """ b, T = x.shape o = self.emb(x).transpose(1, 2) for layer in self.convolutions: o = layer(o) o = o.transpose(1, 2) o = nn.utils.rnn.pack_padded_sequence(o, x_len.cpu(), batch_first=True) self.lstm.flatten_parameters() o, _ = self.lstm(o) o, _ = nn.utils.rnn.pad_packed_sequence(o, batch_first=True) o = o.reshape(b, T * self.state_per_phone, self.in_out_channels) x_len = x_len * self.state_per_phone return o, x_len def inference(self, x, x_len): """Inference to the encoder. Args: x (torch.FloatTensor): input text indices. - shape: :math:`(b, T_{in})` x_len (torch.LongTensor): input text lengths. - shape: :math:`(b,)` Returns: Tuple[torch.FloatTensor, torch.LongTensor]: encoder outputs and output lengths. -shape: :math:`((b, T_{in} * states_per_phone, in_out_channels), (b,))` """ b, T = x.shape o = self.emb(x).transpose(1, 2) for layer in self.convolutions: o = layer(o) o = o.transpose(1, 2) # self.lstm.flatten_parameters() o, _ = self.lstm(o) o = o.reshape(b, T * self.state_per_phone, self.in_out_channels) x_len = x_len * self.state_per_phone return o, x_len class ParameterModel(nn.Module): r"""Main neural network of the outputnet Note: Do not put dropout layers here, the model will not converge. Args: outputnet_size (List[int]): the architecture of the parameter model input_size (int): size of input for the first layer output_size (int): size of output i.e size of the feature dim frame_channels (int): feature dim to set the flat start bias flat_start_params (dict): flat start parameters to set the bias """ def __init__( self, outputnet_size: List[int], input_size: int, output_size: int, frame_channels: int, flat_start_params: dict, ): super().__init__() self.frame_channels = frame_channels self.layers = nn.ModuleList( [Linear(inp, out) for inp, out in zip([input_size] + outputnet_size[:-1], outputnet_size)] ) self.last_layer = nn.Linear(outputnet_size[-1], output_size) self.flat_start_output_layer( flat_start_params["mean"], flat_start_params["std"], flat_start_params["transition_p"] ) def flat_start_output_layer(self, mean, std, transition_p): self.last_layer.weight.data.zero_() self.last_layer.bias.data[0 : self.frame_channels] = mean self.last_layer.bias.data[self.frame_channels : 2 * self.frame_channels] = OverflowUtils.inverse_softplus(std) self.last_layer.bias.data[2 * self.frame_channels :] = OverflowUtils.inverse_sigmod(transition_p) def forward(self, x): for layer in self.layers: x = F.relu(layer(x)) x = self.last_layer(x) return x class Outputnet(nn.Module): r""" This network takes current state and previous observed values as input and returns its parameters, mean, standard deviation and probability of transition to the next state """ def __init__( self, encoder_dim: int, memory_rnn_dim: int, frame_channels: int, outputnet_size: List[int], flat_start_params: dict, std_floor: float = 1e-2, ): super().__init__() self.frame_channels = frame_channels self.flat_start_params = flat_start_params self.std_floor = std_floor input_size = memory_rnn_dim + encoder_dim output_size = 2 * frame_channels + 1 self.parametermodel = ParameterModel( outputnet_size=outputnet_size, input_size=input_size, output_size=output_size, flat_start_params=flat_start_params, frame_channels=frame_channels, ) def forward(self, ar_mels, inputs): r"""Inputs observation and returns the means, stds and transition probability for the current state Args: ar_mel_inputs (torch.FloatTensor): shape (batch, prenet_dim) states (torch.FloatTensor): (batch, hidden_states, hidden_state_dim) Returns: means: means for the emission observation for each feature - shape: (B, hidden_states, feature_size) stds: standard deviations for the emission observation for each feature - shape: (batch, hidden_states, feature_size) transition_vectors: transition vector for the current hidden state - shape: (batch, hidden_states) """ batch_size, prenet_dim = ar_mels.shape[0], ar_mels.shape[1] N = inputs.shape[1] ar_mels = ar_mels.unsqueeze(1).expand(batch_size, N, prenet_dim) ar_mels = torch.cat((ar_mels, inputs), dim=2) ar_mels = self.parametermodel(ar_mels) mean, std, transition_vector = ( ar_mels[:, :, 0 : self.frame_channels], ar_mels[:, :, self.frame_channels : 2 * self.frame_channels], ar_mels[:, :, 2 * self.frame_channels :].squeeze(2), ) std = F.softplus(std) std = self._floor_std(std) return mean, std, transition_vector def _floor_std(self, std): r""" It clamps the standard deviation to not to go below some level This removes the problem when the model tries to cheat for higher likelihoods by converting one of the gaussians to a point mass. Args: std (float Tensor): tensor containing the standard deviation to be """ original_tensor = std.clone().detach() std = torch.clamp(std, min=self.std_floor) if torch.any(original_tensor != std): print( "[*] Standard deviation was floored! The model is preventing overfitting, nothing serious to worry about" ) return std class OverflowUtils: @staticmethod def get_data_parameters_for_flat_start( data_loader: torch.utils.data.DataLoader, out_channels: int, states_per_phone: int ): """Generates data parameters for flat starting the HMM. Args: data_loader (torch.utils.data.Dataloader): _description_ out_channels (int): mel spectrogram channels states_per_phone (_type_): HMM states per phone """ # State related information for transition_p total_state_len = 0 total_mel_len = 0 # Useful for data mean an std total_mel_sum = 0 total_mel_sq_sum = 0 for batch in tqdm(data_loader, leave=False): text_lengths = batch["token_id_lengths"] mels = batch["mel"] mel_lengths = batch["mel_lengths"] total_state_len += torch.sum(text_lengths) total_mel_len += torch.sum(mel_lengths) total_mel_sum += torch.sum(mels) total_mel_sq_sum += torch.sum(torch.pow(mels, 2)) data_mean = total_mel_sum / (total_mel_len * out_channels) data_std = torch.sqrt((total_mel_sq_sum / (total_mel_len * out_channels)) - torch.pow(data_mean, 2)) average_num_states = total_state_len / len(data_loader.dataset) average_mel_len = total_mel_len / len(data_loader.dataset) average_duration_each_state = average_mel_len / average_num_states init_transition_prob = 1 / average_duration_each_state return data_mean, data_std, (init_transition_prob * states_per_phone) @staticmethod @torch.no_grad() def update_flat_start_transition(model, transition_p): model.neural_hmm.output_net.parametermodel.flat_start_output_layer(0.0, 1.0, transition_p) @staticmethod def log_clamped(x, eps=1e-04): """ Avoids the log(0) problem Args: x (torch.tensor): input tensor eps (float, optional): lower bound. Defaults to 1e-04. Returns: torch.tensor: :math:`log(x)` """ clamped_x = torch.clamp(x, min=eps) return torch.log(clamped_x) @staticmethod def inverse_sigmod(x): r""" Inverse of the sigmoid function """ if not torch.is_tensor(x): x = torch.tensor(x) return OverflowUtils.log_clamped(x / (1.0 - x)) @staticmethod def inverse_softplus(x): r""" Inverse of the softplus function """ if not torch.is_tensor(x): x = torch.tensor(x) return OverflowUtils.log_clamped(torch.exp(x) - 1.0) @staticmethod def logsumexp(x, dim): r""" Differentiable LogSumExp: Does not creates nan gradients when all the inputs are -inf yeilds 0 gradients. Args: x : torch.Tensor - The input tensor dim: int - The dimension on which the log sum exp has to be applied """ m, _ = x.max(dim=dim) mask = m == -float("inf") s = (x - m.masked_fill_(mask, 0).unsqueeze(dim=dim)).exp().sum(dim=dim) return s.masked_fill_(mask, 1).log() + m.masked_fill_(mask, -float("inf")) @staticmethod def double_pad(list_of_different_shape_tensors): r""" Pads the list of tensors in 2 dimensions """ second_dim_lens = [len(a) for a in [i[0] for i in list_of_different_shape_tensors]] second_dim_max = max(second_dim_lens) padded_x = [F.pad(x, (0, second_dim_max - len(x[0]))) for x in list_of_different_shape_tensors] return nn.utils.rnn.pad_sequence(padded_x, batch_first=True)